A glimpse into the fungal metabolomic abyss: Novel network analysis reveals relationships between exogenous compounds and their outputs

• Published in: PNAS nexus 2023-10, Vol.2 (10), p.pgad322
• Main Authors: Gopalakrishnan Meena, Muralikrishnan, Lane, Matthew J, Tannous, Joanna, Carrell, Alyssa A, Abraham, Paul E, Giannone, Richard J, Ané, Jean-Michel, Keller, Nancy P, Labbé, Jesse L, Geiger, Armin G, Kainer, David, Jacobson, Daniel A, Rush, Tomás A
• Format: Article
• Language: English
• Subjects: Algorithms; Analysis; Biological, Health, and Medical Sciences; Biosynthesis; Chemical properties; Chemical research; Data mining; Drug discovery; Fungi; Identification and classification; Lipids; Liquid chromatography; Mass spectrometry; Metabolites; Methods; Physiological aspects; Systems Biology
• ISSN: 2752-6542; 2752-6542
• DOI: 10.1093/pnasnexus/pgad322

Fungal specialized metabolites are a major source of beneficial compounds that are routinely isolated, characterized, and manufactured as pharmaceuticals, agrochemical agents, and industrial chemicals. The production of these metabolites is encoded by biosynthetic gene clusters that are often silent under standard growth conditions. There are limited resources for characterizing the direct link between abiotic stimuli and metabolite production. Herein, we introduce a network analysis-based, data-driven algorithm comprising two routes to characterize the production of specialized fungal metabolites triggered by different exogenous compounds: the direct route and the auxiliary route. Both routes elucidate the influence of treatments on the production of specialized metabolites from experimental data. The direct route determines known and putative metabolites induced by treatments and provides additional insight over traditional comparison methods. The auxiliary route is specific for discovering unknown analytes, and further identification can be curated through online bioinformatic resources. We validated our algorithm by applying chitooligosaccharides and lipids at two different temperatures to the fungal pathogen . After liquid chromatography-mass spectrometry quantification of significantly produced analytes, we used network centrality measures to rank the treatments' ability to elucidate these analytes and confirmed their identity through fragmentation patterns or in silico spiking with commercially available standards. Later, we examined the transcriptional regulation of these metabolites through real-time quantitative polymerase chain reaction. Our data-driven techniques can complement existing metabolomic network analysis by providing an approach to track the influence of any exogenous stimuli on metabolite production. Our experimental-based algorithm can overcome the bottlenecks in elucidating novel fungal compounds used in drug discovery.